EMPIRICAL LIKELIHOOD FOR ESTIMATING EQUATIONS WITH MISSING VALUES
By Dong Wang, Songxi Chen. 2009
140 citations recorded by the source corpus.
We consider an empirical likelihood inference for parameters defined by general estimating equations when some components of the random observations are subject to missingness. As the nature of the estimating equations is wide-ranging, we propose a nonparametric imputation of the missing values from a kernel estimator of the conditional distribution of the missing variable given the always observable variable. The empirical likelihood is used to construct a profile likelihood for the parameter of interest. We demonstrate that the proposed nonparametric imputation can remove the selection bias in the missingness and the empirical likelihood leads to more efficient parameter estimation. The proposed method is further evaluated by simulation and an empirical study on a genetic dataset on recombinant inbred mice.
Subfields: Genetics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/5943a8d37e115f165f7e90785714f7b580df2397
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
In Statistics and Probability
- I. Van Keilegom
- Songxi Chen
- Peisong Han
- Tommaso Cai
- Xiaohong Chen
- Wenceslao González-Manteiga
- Olivier Scaillet
- Maria Pacurar
Citations in the corpus
- Combining Inverse Probability Weighting and Multiple Imputation to Improve Robustness of Estimation (2016, methodology): “Non-parametric modelling has been proposed by some researchers as one possible solution (e.g. Cheng, 1994; Wang et al., 1997; Aerts et al., 2002; Zhou et al., 2008; Wang & Chen, 2009).”
- Estimating linear functionals in nonlinear regression with responses missing at random (2009, unknown): “31 02 v1 [ m at h. ST ] 2 1 A ug 2 00 9 The Annals of Statistics 2009, Vol. 37, No. 5A, 2245–2277 DOI: 10.1214/08-AOS642 c© Institute of Mathematical Statistics, 2009”
- A review on empirical likelihood methods for regression (2009, methodology): “As shown in Wang and Chen (2009), Wilks’ theorem is no longer valid for the EL ratio.”